The core of SDN is that, by normalizing the channel statistics which control the global information, the instance normalization (IN) [1]} enforces the content encoder to focus on the linguistic part and remove the global information (i.e, speaker information), while the averge-pool enforces the speaker encoder to focus on the non-linguistic part and learn the global information. Besides, the convolutional layer is used to capture long-term information. The dense layer [2]} is used to enhance feature reuse and network training. And the adaptive instance normalization (AdaIN) [3]} is utilized in decoder to bring the global information to the predicted speech feature though the corresponding parameters provided by speaker encoder. By doing this, the global information needed in the decoder is controlled by the speaker encoder. Thus, the SDN is encouraged to learn factorized representations. The loss of SDN is the reconstruction L1 loss between the input speech feature and the predicted speech feature. IN is expressed as:
\(M_{c}^{^{\prime }}=\frac{M_{c}[w]-u_{c}}{\sigma _{c}},\) 
